The convergence of techniques for sensing, communication, and processing has led to the emergence of wireless sensor networks. Recently, large-scale sensing has become feasible with the use of low-cost, low-energy wireless sensor nodes. Many systems, for example in manufacturing, testing, and monitoring, collect data from a number of wireless sensors. The availability of these sensor networks enables sensing and monitoring of the physical world.
Even more so than in other applications that use wireless data transfer, providing reliable data collection is a paramount concern in sensor networks, as the data is collected, processed, and used to make decisions in a machine-to-machine data collection framework. However, there are well-known problems with wireless data transfer relating to the reliability and correction of data.
For example, a wireless network of sensor nodes is inherently exposed to various sources of unreliability, such as unreliable communication channels, node failures, malicious tampering of nodes, and eavesdropping. Sources of unreliability can be generally classified into two categories: faults that change behavior permanently; and failures that lead to transient deviations from normal behavior, referred to herein as “soft failures”.
Soft failures occur in wireless channels as transient errors, caused by noise at the receiver, channel interference, and/or multi-path fading effects. Additionally, the use of aggressive design technologies such as deep-sub-micron (DSM) and ultra-deep-sub-micron (UDSM) to reduce the cost of each node further exposes the nodes to different types of transient errors in computations and sensing.
Most techniques for gauging reliability of sensor nodes place a high overhead on the collection. Typical existing reliability methods may add redundant hardware or transmit extra data at the source to correct for data corrupted in the circuits or the communication channels respectively. This makes typical methods prohibitively expensive for use with heavily constrained sensor nodes. To address failures in circuits and communication channels, such methods incur high overhead in terms of energy budget, as well as design and manufacturing cost for the sensor nodes.
Other prior methods for data correction include methods to correct soft failures in hardware as well as those to correct bit detection errors on a wireless communication channel. Techniques for correcting soft errors in hardware include both circuit-level and module-level approaches, e.g. triple modular redundancy and error correction coding in hardware. Techniques for correcting bit detection errors on a wireless communication channel include parity-based forward error correction (FEC) coding techniques like channel coding, and retransmission-based techniques like ARQ.